A probabilistic framework for next best view estimation in a cluttered environment

Next best view estimation.Probabilistic framework.Evaluated on multiple exploration scenarios. In this article, we present an information gain-based variant of the next best view problem for occluded environment. Our proposed method utilizes a belief model of the unobserved space to estimate the expected information gain of each possible viewpoint. More precise, this belief model allows a more precise estimation of the visibility of occluded space and with that a more accurate prediction of the potential information gain of new viewing positions. We present experimental evaluation on a robotic platform for active data acquisition, however due to the generality of our approach it also applies to a wide variety of 3D reconstruction problems. With the evaluation done in simulation and on a real robotic platform, exploring and acquiring data from different environments we demonstrate the generality and usefulness of our approach for next best view estimation and autonomous data acquisition.

[1]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Joachim Denzler,et al.  Online Next-Best-View Planning for Accuracy Optimization Using an Extended E-Criterion , 2010, 2010 20th International Conference on Pattern Recognition.

[3]  Wolfram Burgard,et al.  Exploring Unknown Environments with Mobile Robots using Coverage Maps , 2003, IJCAI.

[4]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[5]  Karsten Berns,et al.  Dynamic Frontier Based Exploration with a Mobile Indoor Robot , 2010, ISR/ROBOTIK.

[6]  Dieter Fox,et al.  Autonomous generation of complete 3D object models using next best view manipulation planning , 2011, 2011 IEEE International Conference on Robotics and Automation.

[7]  Sebastian Thrun,et al.  Robotic mapping: a survey , 2003 .

[8]  John Amanatides,et al.  A Fast Voxel Traversal Algorithm for Ray Tracing , 1987, Eurographics.

[9]  Héctor H. González-Baños,et al.  Navigation Strategies for Exploring Indoor Environments , 2002, Int. J. Robotics Res..

[10]  Michael Beetz,et al.  Detecting and segmenting objects for mobile manipulation , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[11]  S SukhatmeGaurav,et al.  A probabilistic framework for next best view estimation in a cluttered environment , 2014 .

[12]  Leif Kobbelt,et al.  Image selection for improved Multi-View Stereo , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  G. Roth,et al.  View planning for automated three-dimensional object reconstruction and inspection , 2003, CSUR.

[14]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[15]  Jan-Michael Frahm,et al.  Next Best View Planning for Active Model Improvement , 2009, BMVC.

[16]  Josef Kittler,et al.  Contextual classification of multispectral pixel data , 1984, Image Vis. Comput..

[17]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[18]  Anders Heyden,et al.  Optimal View Path Planning for Visual SLAM , 2011, SCIA.

[19]  Alexei Makarenko,et al.  An experiment in integrated exploration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[21]  Joachim Hertzberg,et al.  Planning Robot Motion for 3D Digitalization of Indoor Environments , 2002 .

[22]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[23]  Robert B. Fisher,et al.  A Best Next View Selection Algorithm Incorporating a Quality Criterion , 1998, BMVC.

[24]  C. Ian Connolly,et al.  The determination of next best views , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[25]  Francesco Amigoni,et al.  A Multi-Objective Exploration Strategy for Mobile Robots , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[26]  Sven Behnke,et al.  Evaluating the Efficiency of Frontier-based Exploration Strategies , 2010, ISR/ROBOTIK.

[27]  Joachim Denzler,et al.  An Information Theoretic Approach for Next Best View Planning in 3-D Reconstruction , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[28]  Anders Heyden,et al.  Covariance Propagation and Next Best View Planning for 3D Reconstruction , 2012, ECCV.

[29]  Peter K. Allen,et al.  Data acquisition and view planning for 3-D modeling tasks , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  Konstantinos A. Tarabanis,et al.  A survey of sensor planning in computer vision , 1995, IEEE Trans. Robotics Autom..

[31]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[32]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[33]  Richard Pito,et al.  A Solution to the Next Best View Problem for Automated Surface Acquisition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..